Business Intelligence (BI) tools have taken the business world by storm. According to new research, companies that adopt advanced visualization, dashboards, and reporting tools are proven to experience a 26 percent increase in sales

However, many companies aren’t bringing in those dashboards because they actually use them, but rather they’re aiming for the false reassurance that they know everything about their business. . This gets at the heart of some of the weaknesses of traditional BI tools, where even at peak performance, Big Data can miss big issues. 

There are several reasons why traditional BI tools can fall short of confronting problems they are being applied to solve.

Traditional BI Tools Don’t Dive Down into the Details

For reliable decision-making, you need to embrace 100% of your organizational data (both internal and external). Your software need to be able to quickly dive down into the details of data. When BI tool can’t get into the details of data and key metrics from throughout the organization, you won’t be in the position to make decisions when necessary.

One of the primary limitations of traditional BI tools is the lack of granularity for the information parsed. For example, a case study from the Rubicon Project showcases how Rubicon’s lifeblood was its data, needing to quickly analyze billions of data points as real-time ad bidding auctions occurred within 40 milliseconds of one another.

The traditional BI tools that Rubicon used didn’t get to the details. While they were alerted for big events like when entire ad-bidding machines went down, if, however, a client in Asia or Europe was trading at a much higher or lower rate than normal, their tools didn’t alert them to that level of detail. Their data analysts were forced to discern these trends through guesswork and intuition, often well after the anomalous trends had taken place.

Alert Storms Divert Users from Genuine Business Incidents

For data-driven organizations, BI operations teams often spend way too much time wading through a sea of alerts (aka an “alert storm”), most of which are just symptoms of the true cause of an issue. Pinpointing problems with traditional BI tools and trying to identify the difference between a slight deviation and a significant anomaly can be very difficult.

With traffic growing rapidly and changing variably, static thresholds on traditional BI tools need to be continuously increased often to the point of uselessness, constantly triggering alerts. Sometimes these are genuine issues, and sometimes they’re just noise – but traditional BI software can’t tell the difference with either. 

While the constant noise on dashboards can seem comforting, it can cause issues since it’s not truly needed.  With no limitation to the number of data points collected or flexibility for setting alert notifications, it is difficult to differentiate redundant issues from significant ones. Good alert management begins with being able to scale the number of alerts, identifying the significant ones.

False Positives and Reactive Management

The metrics you want to focus on frequently are the handful of business KPIs everyone in your organization needs in front of them. Yet too many recurring alerts can create fatigue. Setting static thresholds triggers too many false positives or having you miss out on genuine anomalies.

Analytics teams can spend excessive amounts of time calibrating alert thresholds manually, all the while losing money because thresholds are different over various channels. They may also miss the signs of performance issues when new services are implemented. As a business grows, more and more incidents can go undetected while trying to make sense of the massive volume of metrics. By creating a lot of dashboards and monitoring daily or weekly reports or by setting upper and lower alert thresholds for each metric leaves a lot of room for human error. Often these approaches fail, identifying too many anomalies (false positives) or not enough (false negatives).

Filtering out minor activity and highlighting the highest-priority risks can provide enough context to drastically diminish false positives and the burdens they place on overworked analyst teams.

Can’t Account for Seasonality

Some, but not all, of a company’s metrics will exhibit aspects of seasonality. This refers to the presence of cyclical patterns in time series data. The period of the cycle can span from hours to a full year or more. The main sources of seasonality could be climate, institutions, social habits and practices, or calendar. 

Seasonal patterns are changes we expect; they are part of the normal behavior of a given metric and thus must be included in the model of that metric. For some metrics, however, there are no seasonal patterns. And sometimes, multiple seasonal patterns are present in a time series. Since this can be misidentified as outliers that might deserve attention, seasonal variability must be identified, filtered out and ignored.

Traditional BI tools are constantly collecting fluctuating data, usually calibrated towards a baseline level of business activity. They aren’t designed to detect a 20 percent loss or gain, but to address pre-determined questions. The static thresholds used in traditional BI tools are meaningless for seasonal data, generating overwhelming alert-storms. Dashboards can’t keep up with these sudden spikes, where the data ends up being yesterday’s news.

Business Insight Latency Delays Problem Discovery

Problems come with the need for real-time analysis. This requires the ability to handle and process high-velocity data in real time – a major challenge when data latency is built into your architecture, as seen in  legacy BI dashboards. They don’t show status in real-time, so most users will only discover business problems after the fact. Business users need actionable insights based on the latest information, so minimizing latency gives analysts the most valid and up-to-date information available for accurate decision-making.

Disrupting the Static Nature of BI Tools

Traditional business intelligence (BI) tools are retrospective. Data may be just a few hours old, but in minutes, a transaction can fail, a customer can leave, or something can crash. Data requires more than traditional approaches to detect and respond to business events as they happen. Due to these limitations, data analysts are only looking at a subset of the data, focusing on just a few key metrics, and struggling to get an integrated view of all business metrics.

It’s like using a flashlight to light up a football stadium. What you really need are floodlights. By taking in the full picture, you can make better decisions to impact business success and improve customer satisfaction.

Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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